Use of a deep learning algorithm for non-mass enhancement on breast MRI: comparison with radiologists’ interpretations at various levels

PURPOSE: To evaluate the diagnostic performance of deep learning using the Residual Networks 50 (ResNet50) neural network constructed from different segmentations for distinguishing malignant and benign non-mass enhancement (NME) on breast magnetic resonance imaging (MRI) and conduct a comparison wi...

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Detalles Bibliográficos
Autores principales: Goto, Mariko, Sakai, Koji, Toyama, Yasuchiyo, Nakai, Yoshitomo, Yamada, Kei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Nature Singapore 2023
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10543141/
https://www.ncbi.nlm.nih.gov/pubmed/37071250
http://dx.doi.org/10.1007/s11604-023-01435-w

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10543141/
https://www.ncbi.nlm.nih.gov/pubmed/37071250
http://dx.doi.org/10.1007/s11604-023-01435-w

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